Variants of history models used for training the general model using data of 40 patients. by Tomasz Gutowski (17200042)

Comparative analysis of model performance indicators and diagnostic visualizations for full-featured and simplified models across ten algorithms. by Aref Andishgar (18155608)

Simulated trajectories from real data, optimal schedule, and nearest optimal schedule. by Samuel Christensen (9922554)

History of the total number of births to U.S. residents per year and the adoption growth of the <i>2003 Revised Birth Certificate</i>. by Karl W. Schulz (14309461)

Data_Sheet_1_An Evolutionary Algorithm to Personalize Stool-Based Colorectal Cancer Screening.pdf by Luuk A. van Duuren (12001607)

The flowchart of the proposed algorithm. by Muhammad Ayyaz Sheikh (18610943)

“…To overcome this limitation, recent advancements have introduced multi-objective evolutionary algorithms for ATS. This study proposes an enhancement to the performance of ATS through the utilization of an improved version of the Binary Multi-Objective Grey Wolf Optimizer (BMOGWO), incorporating mutation. …”

The proposed framework model–HMLFSM. by Murad Al-Rajab (10661360)

“…Machine Learning (ML) algorithms have emerged as a promising approach for non-invasive colon cancer classification using genetic data or patient demographics and medical history. …”

Comparison of the proposed model, with others in the literature. by Murad Al-Rajab (10661360)

Comparison of models performance metrics. by Mukhtar Ijaiya (18935122)

“…Merged demographic, clinic, pharmacy, and laboratory data were included as potential predictor variables in multiple models. …”

The number of relevant genes (features) selected by the proposed model on each phase. by Murad Al-Rajab (10661360)

Predictive model-building process. by Mukhtar Ijaiya (18935122)

“…Merged demographic, clinic, pharmacy, and laboratory data were included as potential predictor variables in multiple models. …”

Comparative performance metrics of ML models. by Rajeev Bopche (20208606)

“…By avoiding reliance on real-time clinical data, our model allows for enhanced scalability across various healthcare settings, including resource-limited environments. …”

Schematic architecture of the proposed hybrid SVM-PSO-AI framework for cardiovascular risk prediction: It has four input vectors V1 (Medical Imaging), V2 (Clinical Data), V3 (Genom... by Medhat A. Tawfeek (22522087)

Parameter Settings for the Proposed Hybrid Model. The Particle Swarm Optimization (PSO) parameters (swarm size, iterations, inertia weight, cognitive and social coefficient) were e... by Medhat A. Tawfeek (22522087)

Description, feature counts, and data partitioning of the MIMIC-III clinical database (v1.4) used in this study. The table categorizes patient data into three main relevant modalit... by Medhat A. Tawfeek (22522087)

Consort diagram for the study. by Nicola Lazzarini (3528197)

Subjects:

Performance confidence intervals. by Nicola Lazzarini (3528197)

Subjects:

Baseline characteristics of patients in the study cohort. by Nicola Lazzarini (3528197)

Subjects:

Input features. by Nicola Lazzarini (3528197)

Subjects:

Most important predictive features. by Nicola Lazzarini (3528197)

Subjects: